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RISS 인기검색어
- 검색키워드
- 저자 : Abdul Aziz Abdul Samad (검색결과 3 건)
- 무료
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Repair of precracked RC rectangular shear beams using CFRP strip technique
Abdul Aziz Abdul Samad,Abang Abdullah Abang Ali,J. Jayaprakash,Ashrabov Anvar Abbasovich 국제구조공학회 2007 Structural Engineering and Mechanics, An Int'l Jou Vol.26 No.4
The exploitation of fibre reinforced polymer composites, as external reinforcement is an evergreen and well-known technique for improving the structural performance of reinforced concrete structures. The demand to use FRP composites in the civil engineering industry is mainly due to its high strength, light weight, and stiffness. This paper exemplifies the shear strength of partially precracked reinforced concrete rectangular beams repaired with externally bonded Bi-Directional Carbon Fibre Reinforced Polymer (CFRP) Fabrics strips. All specimens were cast in the laboratory environment without any internal shear reinforcement. The test parameters were longitudinal tensile reinforcement, shear span to effective depth ratio, spacing of CFRP strips, and orientation of CFRP reinforcement. It mainly focuses on the shear capacity and modes of failure of the CFRP strengthened shear beams. Results have shown that the CFRP repaired beams attained a shear enhancement of 32% and 107.64% greater than the control beams. This study underscores that the CFRP strip technique significantly enhanced the shear capacity of precracked reinforced concrete rectangular beams without any internal shear reinforcement.
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Repair of precracked RC rectangular shear beams using CFRP strip technique
Jayaprakash, J.,Samad, Abdul Aziz Abdul,Abbasovich, Ashrabov Anvar,Ali, Abang Abdullah Abang Techno-Press 2007 Structural Engineering and Mechanics, An Int'l Jou Vol.26 No.4
The exploitation of fibre reinforced polymer composites, as external reinforcement is an evergreen and well-known technique for improving the structural performance of reinforced concrete structures. The demand to use FRP composites in the civil engineering industry is mainly due to its high strength, light weight, and stiffness. This paper exemplifies the shear strength of partially precracked reinforced concrete rectangular beams repaired with externally bonded Bi-Directional Carbon Fibre Reinforced Polymer (CFRP) Fabrics strips. All specimens were cast in the laboratory environment without any internal shear reinforcement. The test parameters were longitudinal tensile reinforcement, shear span to effective depth ratio, spacing of CFRP strips, and orientation of CFRP reinforcement. It mainly focuses on the shear capacity and modes of failure of the CFRP strengthened shear beams. Results have shown that the CFRP repaired beams attained a shear enhancement of 32% and 107.64% greater than the control beams. This study underscores that the CFRP strip technique significantly enhanced the shear capacity of precracked reinforced concrete rectangular beams without any internal shear reinforcement.
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Malware Classification using Dynamic Analysis with Deep Learning
Asad Amin,Muhammad Nauman Durrani,Nadeem Kafi,Fahad Samad,Abdul Aziz International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.8
There has been a rapid increase in the creation and alteration of new malware samples which is a huge financial risk for many organizations. There is a huge demand for improvement in classification and detection mechanisms available today, as some of the old strategies like classification using mac learning algorithms were proved to be useful but cannot perform well in the scalable auto feature extraction scenario. To overcome this there must be a mechanism to automatically analyze malware based on the automatic feature extraction process. For this purpose, the dynamic analysis of real malware executable files has been done to extract useful features like API call sequence and opcode sequence. The use of different hashing techniques has been analyzed to further generate images and convert them into image representable form which will allow us to use more advanced classification approaches to classify huge amounts of images using deep learning approaches. The use of deep learning algorithms like convolutional neural networks enables the classification of malware by converting it into images. These images when fed into the CNN after being converted into the grayscale image will perform comparatively well in case of dynamic changes in malware code as image samples will be changed by few pixels when classified based on a greyscale image. In this work, we used VGG-16 architecture of CNN for experimentation.
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